123
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Detecting mutual fund timing ability using the threshold model

, &
Pages 829-834 | Published online: 21 Aug 2006
 

Abstract

This paper proposes a new method based on threshold regression to test mutual fund market-timing abilities. The traditional Henriksson and Merton model is shown to represent only a special case within the proposed model. The potential bias of using the traditional model is demonstrated and it is argued that the proposed model provides more accurate inferences on the market-timing effects of mutual funds. The empirical results for a set of randomly-selected US mutual funds indicate the superior performance of the proposed method in detecting the market-timing ability.

Notes

1 Treynor and Mazuy (Citation1966), Henriksson and Merton (Citation1981) and Chang and Lewellen (Citation1984) noted that investment managers have superior information and forecasting skills.

2 The results are omitted to save space. However, they are available upon request.

3 The regression results of the threshold effect from Equation Equation6 are omitted for saving space. Sixteen of the mutual funds exhibited a positive and significant value for , indicating that the fund manager has stock-selection ability based upon the threshold effect. Four of the mutual funds also exhibited a positive and significant value for , indicating that the fund manager has market-timing ability based upon the threshold effect.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 205.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.